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School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China





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Academic Editor: Vittorio M. N. Passaro

Abstract Multi-sensor sonar tracking has many advantages, such as the potential to reduce the overall measurement uncertainty and the possibility to hide the receiver. However, the use of multi-target multi-sensor sonar tracking is challenging because of the complexity of the underwater environment, especially the low target detection probability and extremely large number of false alarms caused by reverberation. In this work, to solve the problem of multi-target multi-sensor sonar tracking in the presence of clutter, a novel probabilistic multi-hypothesis tracker PMHT approach based on the extended Kalman filter EKF and unscented Kalman filter UKF is proposed. The PMHT can efficiently handle the unknown measurements-to-targets and measurements-to-transmitters data association ambiguity. The EKF and UKF are used to deal with the high degree of nonlinearity in the measurement model. The simulation results show that the proposed algorithm can improve the target tracking performance in a cluttered environment greatly, and its computational load is low. View Full-Text

Keywords: probabilistic multi-hypothesis tracker PMHT; multi-target multi-sensor sonar tracking; extended Kalman filter EKF; unscented Kalman filter UKF; data association probabilistic multi-hypothesis tracker PMHT; multi-target multi-sensor sonar tracking; extended Kalman filter EKF; unscented Kalman filter UKF; data association





Autor: Xiaohua Li * , Yaan Li, Jing Yu, Xiao Chen and Miao Dai

Fuente: http://mdpi.com/



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